A Neural Model for User Geolocation and Lexical Dialectology

Abstract

We propose a simple yet effective textbased user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

“…It not only improves prediction accuracy but also greatly reduces mean error distance. Compared with a strong neural model equipped with local dialects (Rahimi et al, 2017), it increases Acc@161 by an absolute value 4% and reduces mean error distance by about 400 kilometers on the challenging Twitter-World dataset, without using any external knowledge. Its mean error distance on Twitter-World is even comparable to some methods using network feature (Do et al, 2017).…”

“…In recent years, neural network based prediction methods have shown great success on this Twitter user geolocation prediction task (Rahimi et al, 2017;Miura et al, 2017). However, these neural network based methods largely ignore the hierarchical structure among locations (eg.…”

Geocoding Without Geotags: A Text-based Approach for reddit

“…Based on within-domain performance for each of the Twitter data sets, we recognize that our inference modeling approach is below state of the art. For example, in the space of text-only models, Rahimi et al (2017) have achieved an Acc@100 of 0.34 on TWITTER-WORLD using a multilayer perceptron and k-d tree discretization over the label set.…”

Twitter User Geolocation Using Deep Multiview Learning

“…The weakness of this method is that it can not propagate labels (locations) to users who are not connected to the graph. To address this problem, methods combining textual information and graph topology knowledge are proposed in [24], [8]. Furthermore, these works build densely undirected graphs based on mentioning of users, which helps improve significantly the results.…”

“…Our user graph is formed in a way similar as in [8], [24] but instead of predicting users' locations directly on the graph, we extract node2vec feature for later use in our model. First, a unique set of nodes, V , is created for all the users of interest.…”